The measurement accuracy of electric energy meters (EEMs) is crucial for respecting the fairness of the electricity market and the justice of electric energy settlement. However, the basic error (BE) of the measurement suffers from external stress, especially under extreme environments. It is challenging to evaluate the degradation trend of EEMs under multi-stress. To address this issue, this paper proposes modeling and evaluating methods for precise degradation analysis of EEMs under multi-stress. First, an optimized k-nearest neighbor (OKNN) is proposed to correct the outliers, where the stress-related weight and weighting factor are designed. Then, a varied-bias wiener process with hierarchical Bayesian (VWHB) model is introduced to evaluate and explore the impact of stress on the BE. The multi-stress, as well as the BE, are fused to parameterize this impact. Integrating the OKNN and VWHB, a degradation analysis framework is further proposed to model the data and conduct the degradation analysis. Extensive experiments on field data collected from the typical operating environment lab demonstrated that the proposed degradation analysis framework reveals superior performance than some state-of-art approaches. The root of the mean of the square of errors and the mean of absolute value of errors of OKNN-VWHB are the lowest with 0.0356 and 0.0281, respectively, for all the EEMs from three different companies.

Modular and truss robots offer the potential of high reconfigurability and great functional flexibility, but common implementations relying on rigid components often lead to highly complex actuation and control requirements. This paper introduces a new type of modular, compliant robot: TrussBot. TrussBot is composed of 3D-printed tetrahedral modules connected at the corners with compliant joints. We propose a truss geometry, analyze its deformation modes, and provide a simulation framework for predicting its behavior under applied loads and actuation. The TrussBot is geometrically constrained, thus requiring compliant joints to move. The TrussBot can be actuated through a network of tendons which pinch vertices together and apply a twisting motion due to the structure's connectivity. The truss was demonstrated in a physical prototype and compared to simulation results.

The failure evaluation of electric energy metering equipment is essential for the equipment design and accurate measurement of electric energy, especially in extreme environmental stress. However, actual failure assessment is often affected by the environmental noise and insufficient interpretability. To address this problem, this article first proposes an improved k-nearest neighbor (IkNN) to identify potential outliers. In addition, an optimized distance function is used to obtain the score for each outlier. Next, a probability analysis method, namely, the weighted fusion Bayesian (WFB), is proposed to fuse multiple extreme environmental stresses and failure rate using the proposed nonlinear fusion function. Combining the WFB and the IkNN, examples from three extreme environmental regions show that the proposed evaluation framework has a higher assessment performance and less uncertainty. Compared with the classical prediction methods, our framework has profound outlier detection and failure prediction performance ever under the condition of small samples. More importantly, the parameters of this model are interpretable compared to some conventional approaches.